import os
import numpy as np
import pandas as pd
from glob import glob
from pathlib import Path

# image
import cv2
from skimage.io import imread

# TensorFlow
import tensorflow as tf
from tensorflow.keras import layers, models

# Visualisation libraries

## Text
from colorama import Fore, Back, Style
from IPython.display import Image, display, Markdown, Latex, clear_output

## progressbar
from tqdm import tqdm

## plotly
from plotly.offline import init_notebook_mode, iplot 
import plotly.graph_objs as go
import plotly.offline as py
from plotly.subplots import make_subplots
import plotly.express as px

## maps
import folium
from folium import plugins
from folium.plugins import HeatMap

## seaborn
import seaborn as sns

## matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon
from matplotlib.font_manager import FontProperties
import matplotlib.colors as mcolors
from matplotlib.colors import LinearSegmentedColormap
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from matplotlib import cm
plt.style.use('seaborn-whitegrid')
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
plt.rcParams['text.color'] = 'k'
%matplotlib inline

import warnings
warnings.filterwarnings("ignore")

Home / Natural Processes Analytics and Modeling

Satellite Images Of Hurricane Damage

Dataset Description

Overview

The data are satellite images from Texas after Hurricane Harvey divided into two groups (damage and no_damage). The goal is to make a model which can automatically identify if a given region is likely to contain flooding damage.

def Header(Text, L = 100, C = 'Blue', T = 'White'):
    BACK = {'Black': Back.BLACK, 'Red':Back.RED, 'Green':Back.GREEN, 'Yellow': Back.YELLOW, 'Blue': Back.BLUE,
         'Magenta':Back.MAGENTA, 'Cyan': Back.CYAN}
    FORE = {'Black': Fore.BLACK, 'Red':Fore.RED, 'Green':Fore.GREEN, 'Yellow':Fore.YELLOW, 'Blue':Fore.BLUE,
         'Magenta':Fore.MAGENTA, 'Cyan':Fore.CYAN, 'White': Fore.WHITE}
    print(BACK[C] + FORE[T] + Style.NORMAL + Text + Style.RESET_ALL + ' ' + FORE[C] +
          Style.NORMAL +  (L- len(Text) - 1)*'=' + Style.RESET_ALL)
    
def Line(L=100, C = 'Blue'):
    FORE = {'Black': Fore.BLACK, 'Red':Fore.RED, 'Green':Fore.GREEN, 'Yellow':Fore.YELLOW, 'Blue':Fore.BLUE,
         'Magenta':Fore.MAGENTA, 'Cyan':Fore.CYAN, 'White': Fore.WHITE}
    print(FORE[C] + Style.NORMAL + L*'=' + Style.RESET_ALL)
    
Start_Path = 'images_hur'
Target = 'Damage'
Data = pd.DataFrame({'Path': list(Path(Start_Path).glob('**/*.jp*g'))})
Data['Path'].map(lambda x: x.parent.stem.title().replace('_',' '))
Data['File'] = Data['Path'].map(lambda x: str(x).split('\\')[-1])
Data[Target] = Data['Path'].map(lambda x: x.parent.stem.title().replace('_',' '))
Data['Dataset'] = Data['Path'].map(lambda x: x.parent.parent.stem)
Data = Data.loc[Data['Dataset'] != 'test_another']                                   
Data['Dataset'] = Data['Dataset'].map(lambda x: x.title().replace('_Another',''))
Data['Latitude'] = Data['Path'].map(lambda x: x.stem.split('_')[0]).astype('float')
Data['Longitude'] = Data['Path'].map(lambda x: x.stem.split('_')[1]).astype('float')
#
Header('A Sample of the Dataframe')
Data = Data.reindex(sorted(Data.columns), axis=1)
display(Data.sample(10))
display(pd.DataFrame({'Number of Instances':[Data.shape[0]], 'Number of Attributes':[Data.shape[1]]}).style.hide_index())
#
def Path_Tree(startpath, Extension, sep = ' ' * 3, n = 5):
    Folders_with_Images = []
    C = ['Red', 'Green', 'Magenta', 'Cyan']*len(os.listdir(startpath))
    BACK = {'Black': Back.BLACK, 'Red':Back.RED, 'Green':Back.GREEN,
            'Yellow': Back.YELLOW, 'Blue': Back.BLUE,
            'Magenta':Back.MAGENTA, 'Cyan': Back.CYAN}
    for root, _, files in os.walk(startpath):
        level = root.replace(startpath, '').count(os.sep)
        if level >0:
            indent = sep* (level)+ '└──'
            print(indent +  BACK[C[level]] + Fore.BLACK + Style.NORMAL + os.path.basename(root) + Style.RESET_ALL)
        else:
            title = os.path.basename(root)
            print(Style.RESET_ALL + Fore.BLUE + Style.NORMAL + '=' * (len(title) +1) + Style.RESET_ALL)
            print(Back.BLACK + Fore.CYAN + Style.NORMAL + title + Style.RESET_ALL)
            print(Style.RESET_ALL + Fore.BLUE + Style.NORMAL + '=' * (len(title) +1)+ Style.RESET_ALL)
        subindent = ' ' * 4 * (level + 1)
        for file in files[:1]:
            if file.endswith(Extension):
                Folders_with_Images.append(root)
                List = os.listdir(root)
                print(level* sep, Fore.BLUE + Style.NORMAL + 
                      '%i %s files: ' % (len(List), List[0].split('.')[-1].upper()) + Style.RESET_ALL +
                      '%s'%', '.join(List[:n]) + ', ...')
    return Folders_with_Images
_ = Folders_with_Images = Path_Tree(Start_Path, '.jpeg', n = 3)

A Sample of the Dataframe ==========================================================================

	Damage	Dataset	File	Latitude	Longitude	Path
11775	Damage	Train	-95.08003599999999_29.828667.jpeg	-95.080036	29.828667	images_hur\train_another\damage\-95.0800359999...
13639	Damage	Train	-95.595192_29.765008.jpeg	-95.595192	29.765008	images_hur\train_another\damage\-95.595192_29....
21968	Damage	Validation	-96.968689_28.816515000000003.jpeg	-96.968689	28.816515	images_hur\validation_another\damage\-96.96868...
14453	Damage	Train	-95.632126_29.775575.jpeg	-95.632126	29.775575	images_hur\train_another\damage\-95.632126_29....
21184	Damage	Validation	-95.086515_29.827463.jpeg	-95.086515	29.827463	images_hur\validation_another\damage\-95.08651...
19126	No Damage	Train	-95.635625_29.844209999999997.jpeg	-95.635625	29.844210	images_hur\train_another\no_damage\-95.635625_...
11156	Damage	Train	-93.742164_30.122063.jpeg	-93.742164	30.122063	images_hur\train_another\damage\-93.742164_30....
19351	No Damage	Train	-95.638134_29.838812.jpeg	-95.638134	29.838812	images_hur\train_another\no_damage\-95.638134_...
35	Damage	Test	-93.734053_29.788496000000002.jpeg	-93.734053	29.788496	images_hur\test\damage\-93.734053_29.788496000...
22264	No Damage	Validation	-95.276788_29.6097.jpeg	-95.276788	29.609700	images_hur\validation_another\no_damage\-95.27...

Number of Instances	Number of Attributes
14000	6

===========
images_hur
===========
   └──test
      └──damage
       1000 JPEG files: -93.548123_30.900623.jpeg, -93.560128_30.894917.jpeg, -93.578271_30.779923999999998.jpeg, ...
      └──no_damage
       1000 JPEG files: -95.061894_30.007746.jpeg, -95.061936_29.828088.jpeg, -95.062123_30.056714000000003.jpeg, ...
   └──test_another
      └──damage
       8000 JPEG files: -93.528502_30.987438.jpeg, -93.5302_30.988157.jpeg, -93.53950999999999_30.982944.jpeg, ...
      └──no_damage
       1000 JPEG files: -95.062321_30.060401000000002.jpeg, -95.062533_30.055047.jpeg, -95.062589_29.830265.jpeg, ...
   └──train_another
      └──damage
       5000 JPEG files: -93.55964_30.895018.jpeg, -93.573763_30.693981.jpeg, -93.578271_30.779923999999998.jpeg, ...
      └──no_damage
       5000 JPEG files: -95.061275_29.831535.jpeg, -95.061412_29.826522999999998.jpeg, -95.061825_29.828608000000003.jpeg, ...
   └──validation_another
      └──damage
       1000 JPEG files: -93.558326_30.895248.jpeg, -93.563851_30.894492.jpeg, -93.57315600000001_30.994087.jpeg, ...
      └──no_damage
       1000 JPEG files: -95.061894_30.007746.jpeg, -95.062123_30.056714000000003.jpeg, -95.062218_30.055870000000002.jpeg, ...

Dataset Distribution

def DatasetDist(Inp, Target, PD):
    Table = Inp[Target].value_counts().to_frame('Count').reset_index(drop = False).rename(columns = {'index':Target})
    Table = Table.sort_values(by = [Target])
    Table['Percentage'] = np.round(100*(Table['Count']/Table['Count'].sum()), 2)
    
    fig = make_subplots(rows=1, cols=2, horizontal_spacing = 0.02, column_widths=PD['column_widths'],
                        specs=[[{"type": "table"},{"type": "pie"}]])

    # Right
    fig.add_trace(go.Pie(labels=Table[Target].values, values=Table['Count'].values,
                         pull=PD['pull'], textfont=dict(size= PD['textfont']),
                         marker=dict(colors = [Colors_dict[x] for x in Table[Target].unique()],
                                     line=dict(color='black', width=1))), row=1, col=2)
    fig.update_traces(hole=PD['hole'])
    fig.update_layout(height = PD['height'], legend=dict(orientation=PD['legend_orientation']),
                      legend_title_text= PD['legend_title'])
    
    # Left
    T = Table.copy()
    T['Percentage'] = T['Percentage'].map(lambda x: '%%%.2f' % x)
    Temp = []
    for i in T.columns:
        Temp.append(T.loc[:,i].values)

    fig.add_trace(go.Table(header=dict(values = list(Table.columns), line_color='darkslategray',
                                       fill_color= PD['TableColors'][0], align=['center','center'],
                                       font=dict(color='white', size=12), height=25), columnwidth = PD['tablecolumnwidth'],
                           cells=dict(values=Temp, line_color='darkslategray',
                                      fill=dict(color= [PD['TableColors'][1], PD['TableColors'][1]]),
                                  align=['center','center', 'center'], font_size=12, height=20)), 1, 1)

    fig.update_layout(title={'text': '<b>' + PD['Title'] + '<b>', 'x':PD['title_x'],
                                     'y':PD['title_y'], 'xanchor': 'center', 'yanchor': 'top'})
    fig.show()
    
Colors_dict = dict(zip(Data[Target].unique().tolist(),['DarkRed', 'DarkGreen']))
Pull = [0 for x in range((len(Data[Target].unique().tolist())-1))]
Pull.append(.05)
PD = dict(PieColors = Colors_dict, TableColors = ['DarkGreen','GhostWhite'], hole = .4,
          column_widths=[0.5, 0.5],textfont = 14, height = 350, tablecolumnwidth = [.1, .05, .08], pull = Pull,
          legend_title = Target, legend_orientation = 'v',
          Title ='Train Set', title_x = 0.5, title_y = 0.84)
del Pull
DatasetDist(Data.loc[Data['Dataset'] == 'Train'], Target = Target, PD = PD)
PD.update(dict(TableColors = ['Indigo','GhostWhite'], Title ='Test Set'))
DatasetDist(Data.loc[Data['Dataset'] == 'Test'], Target = Target, PD = PD)

fig, ax = plt.subplots(4, 5 , figsize = (12, 12))
_ = fig.suptitle('A Sample of Train Dataset', fontweight='bold', fontsize = 18)
ax = ax.ravel()
for i, row in Data.loc[Data['Dataset'] == 'Train'].sample(len(ax)).reset_index(drop =True).iterrows():
    _ = ax[i].imshow(imread(row['Path']))
    _ = ax[i].set_title('%s' % row[Target], fontweight='bold', fontsize = 12, color = Colors_dict [row[Target]])
    _ = ax[i].axis("off")
    _ = ax[i].set_aspect(1)
fig.tight_layout()

fig, ax = plt.subplots(1, 1, figsize=(12, 12))
_ = sns.scatterplot(data=Data, x='Latitude', y='Longitude', hue='Dataset', style='Damage', ax = ax, s= 50, alpha = 0.5)
_ = ax.set_xlabel('Latitude')
_ = ax.set_ylabel('Longitude')
_ = ax.grid(False)
_ = ax.set_xlim([-98, -93])
_ = ax.set_ylim([28, 32])
_ = ax.set_title('Image Location by Damage and Dataset Group', weight = 'bold', fontsize = 16)

batch_size = 128
(Img_Height, Img_Width, _) = imread(Data['Path'][0]).shape

Header('Train Images')
train_ds = tf.keras.preprocessing.image_dataset_from_directory(directory= 'images_hur\\train_another',
                                                               shuffle=True,
                                                               seed=123,
                                                               image_size=(Img_Height, Img_Width),
                                                               batch_size=batch_size)
Header('Validation Images Data Generator', C = 'Green')
val_ds = tf.keras.preprocessing.image_dataset_from_directory(directory= 'images_hur\\test',
                                                             image_size=(Img_Height, Img_Width),
                                                             batch_size=batch_size)
Line()

Train Images =======================================================================================
Found 10000 files belonging to 2 classes.
Validation Images Data Generator ===================================================================
Found 2000 files belonging to 2 classes.
====================================================================================================

Modeling: Keras Multi-layer Perceptron (MLP) for Image Classifications

A multi-layer perceptron (MLP) is a class of feedforward artificial neural network (ANN). The algorithm at each iteration uses the SparseCategoricalCrossentropy to measure the loss, and then the gradient and the model update is calculated. At the end of this iterative process, we would reach a better level of agreement between test and predicted sets since the error would be lower from that of the first step.

num_classes = len(Data[Target].unique())
data_augmentation = tf.keras.Sequential([layers.experimental.preprocessing.RandomFlip("horizontal", 
                                                                                   input_shape=(Img_Height, Img_Width, 3)),
                                      layers.experimental.preprocessing.RandomRotation(0.1),
                                      layers.experimental.preprocessing.RandomZoom(0.1)])

model = models.Sequential(name = 'Multi_Class_MLP',
                          layers = [data_augmentation,
                                    layers.experimental.preprocessing.Rescaling(1./255, input_shape=(Img_Height, Img_Width, 3)),
                                    layers.Conv2D(16, 3, padding='same', activation='relu'),
                                    layers.MaxPooling2D(),
                                    layers.Conv2D(32, 3, padding='same', activation='relu'),
                                    layers.MaxPooling2D(),
                                    layers.Conv2D(64, 3, padding='same', activation='relu'),
                                    layers.MaxPooling2D(),
                                    layers.Flatten(),
                                    layers.Dense(128, activation='relu'),
                                    layers.Dense(num_classes)])
model.summary()
tf.keras.utils.plot_model(model, show_shapes=True, show_dtype=True,
                          show_layer_names=True, expand_nested = False, rankdir= 'LR')

Model: "Multi_Class_MLP"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
sequential (Sequential)      (None, 128, 128, 3)       0         
_________________________________________________________________
rescaling (Rescaling)        (None, 128, 128, 3)       0         
_________________________________________________________________
conv2d (Conv2D)              (None, 128, 128, 16)      448       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 64, 64, 16)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 64, 64, 32)        4640      
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 32, 32, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 32, 32, 64)        18496     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 16, 16, 64)        0         
_________________________________________________________________
flatten (Flatten)            (None, 16384)             0         
_________________________________________________________________
dense (Dense)                (None, 128)               2097280   
_________________________________________________________________
dense_1 (Dense)              (None, 2)                 258       
=================================================================
Total params: 2,121,122
Trainable params: 2,121,122
Non-trainable params: 0
_________________________________________________________________

Compiling and fitting the model

# Number of iterations
IT = 21

model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])

# Training the model
history = model.fit(train_ds, validation_data=val_ds, epochs=IT, verbose = 0,
                   callbacks=[tf.keras.callbacks.EarlyStopping(patience=4, restore_best_weights=True)])

def Search_List(Key, List): return [s for s in List if Key in s]
Metrics_Names = dict(zip(model.metrics_names, [x.replace('_',' ').title() for x in model.metrics_names]))

def Table_modify(df, Metrics_Names = Metrics_Names):
    df = df.rename(columns = Metrics_Names)
    df = df.reindex(sorted(df.columns), axis=1)
    df.insert(loc = 0, column = 'Iteration', value = np.arange(0, df.shape[0]), allow_duplicates=False)
    return df

Validation_Table = Search_List('val_',history.history.keys()) 
Train_Table = list(set( history.history.keys()) - set(Validation_Table))
Validation_Table = pd.DataFrame(np.array([history.history[x] for x in Validation_Table]).T, columns = Validation_Table)
Train_Table = pd.DataFrame(np.array([history.history[x] for x in Train_Table]).T, columns = Train_Table)
Validation_Table.columns = [x.replace('val_','') for x in Validation_Table.columns]

Train_Table = Table_modify(Train_Table)
Validation_Table = Table_modify(Validation_Table)

# Train Set Score
score = model.evaluate(train_ds, batch_size = batch_size, verbose = 0)
score = pd.DataFrame(score, index = model.metrics_names).T
score.index = ['Train Set Score']
# Validation Set Score
Temp = model.evaluate(val_ds, batch_size = batch_size, verbose = 0) 
Temp = pd.DataFrame(Temp, index = model.metrics_names).T
Temp.index = ['Validation Set Score']
score = score.append(Temp)
score.rename(columns= Metrics_Names, inplace = True)
score = score.reindex(sorted(score.columns), axis=1)
display(score.style.set_precision(4))

	Accuracy	Loss
Train Set Score	0.9468	0.1306
Validation Set Score	0.9455	0.1404

def Plot_history(history, PD, Title = False, metrics_names = list(Metrics_Names.values())):
    fig = make_subplots(rows=1, cols=2, horizontal_spacing = 0.02, column_widths=[0.6, 0.4],
                        specs=[[{"type": "scatter"},{"type": "table"}]])
    # Left
    Colors = ['OrangeRed', 'MidnightBlue', 'purple']
    for j in range(len(metrics_names)):
        fig.add_trace(go.Scatter(x= history['Iteration'].values, y= history[metrics_names[j]].values,
                                 line=dict(color=Colors[j], width= 1.5), name = metrics_names[j]), 1, 1)
    fig.update_layout(legend=dict(x=0, y=1.1, traceorder='reversed', font_size=12),
                  dragmode='select', plot_bgcolor= 'white', height=600, hovermode='closest',
                  legend_orientation='h')
    fig.update_xaxes(range=[history.Iteration.min(), history.Iteration.max()],
                     showgrid=True, gridwidth=1, gridcolor='Lightgray',
                     showline=True, linewidth=1, linecolor='Lightgray', mirror=True, row=1, col=1)
    fig.update_yaxes(range=[0, PD['yLim']], showgrid=True, gridwidth=1, gridcolor='Lightgray',
                     showline=True, linewidth=1, linecolor='Lightgray', mirror=True, row=1, col=1)
    # Right
    if not PD['Table_Rows'] == None:
        ind = np.linspace(0, history.shape[0], PD['Table_Rows'], endpoint = False).round(0).astype(int)
        ind = np.append(ind, history.index[-1])
        history = history[history.index.isin(ind)]
    T = history.copy()
    T[metrics_names] = T[metrics_names].applymap(lambda x: '%.4e' % x)
    Temp = []
    for i in T.columns:
        Temp.append(T.loc[:,i].values)
    TableColors = PD['TableColors']
    fig.add_trace(go.Table(header=dict(values = list(history.columns), line_color=TableColors[0],
                   fill_color=TableColors[0], align=['center','center'], font=dict(color=TableColors[1], size=12), height=25),
                   columnwidth = PD['tablecolumnwidth'], cells=dict(values=Temp, line_color=TableColors[0],
                      fill=dict(color=[TableColors[1], TableColors[1]]),
                      align=['center', 'center'], font_size=12,height=20)), 1, 2)
    if Title != False:
        fig.update_layout(plot_bgcolor= 'white',
                      title={'text': Title, 'x':0.46, 'y':0.94, 'xanchor': 'center', 'yanchor': 'top'},
                          yaxis_title='Frequency')
    fig.show()

PD = dict(Table_Rows = 25, yLim = 1, tablecolumnwidth = [0.3, 0.4, 0.4], TableColors = ['DarkSlateGray','White'])
Plot_history(Train_Table, Title = 'Train Set', PD = PD)
Plot_history(Validation_Table, Title = 'Validation Set', PD = PD)

val_images = []
val_labels = []
for x, y in list(val_ds):
    val_images.extend(x)
    val_labels.extend(y)
Pred = model.predict(val_ds)
class_names = val_ds.class_names
class_names = [x.title() for x in class_names]
Prob = np.array(tf.nn.softmax(Pred[0]))
for i in tqdm(range(len(Pred[1:]))):
    Temp = np.array( tf.nn.softmax(Pred[i]))
    Prob = np.column_stack((Prob, Temp))

Pred = pd.DataFrame(data = Prob.T, columns = class_names)
Pred['Actual Label'] = [class_names[x] for x in val_labels]

100%|███████████████████████████████████████████████████████████████████████████| 1999/1999 [00:00<00:00, 28550.85it/s]

display(Pred.round(2))

	Damage	No_Damage	Actual Label
0	0.00	1.00	Damage
1	0.00	1.00	Damage
2	1.00	0.00	No_Damage
3	0.00	1.00	No_Damage
4	0.68	0.32	No_Damage
...	...	...	...
1995	0.00	1.00	Damage
1996	0.00	1.00	Damage
1997	0.99	0.01	No_Damage
1998	0.00	1.00	No_Damage
1999	0.00	1.00	Damage

2000 rows × 3 columns

---

References

1. Kaggle Dataset: Satellite Images of Hurricane Damage
2. Tensorflow API Documentation
3. Quoc Dung Cao, Youngjun Choe, December 13, 2018, "Detecting Damaged Buildings on Post-Hurricane Satellite Imagery Based on Customized Convolutional Neural Networks", IEEE Dataport, doi: https://dx.doi.org/10.21227/sdad-1e56.

---